File size: 1,361 Bytes
bd00d6e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 13 14:03:54 2024
@author: rezer
"""
from sdeval.fidelity import CCIPMetrics
from sdeval.controllability import BikiniPlusMetrics
from sdeval.corrupt import AICorruptMetrics
import os
ccip = CCIPMetrics(images=r'jerry_test\train\1_1girl')
bp = BikiniPlusMetrics(
tag_blacklist=[
'bangs', 'long_hair', 'blue_eyes', 'animal_ears', 'sleeveless',
'breasts', 'grey_hair', 'medium_breasts'
]
)
metrics = AICorruptMetrics()
lora_base_name_list=["surtr_arknights-000010",
"surtr_arknights-000012",
"surtr_arknights-000014",
"surtr_arknights-000016",
"surtr_arknights-000018",
"surtr_arknights-000020",
"surtr_arknights-000022",
"surtr_arknights",]
base_path=r'jerry_test'
import pandas as pd
l=[]
for lora_base_name in lora_base_name_list:
test_image_dir=os.path.join(base_path,lora_base_name)
ccip_score=ccip.score( test_image_dir)
metrics_score=metrics.score(test_image_dir)
bp_score=bp.score(test_image_dir)
score=[lora_base_name,ccip_score,metrics_score,bp_score]
print(f"lora_name:{lora_base_name},ccip:{ccip_score},bp:{bp_score},AI-C:{metrics_score}")
l.append(score)
pd.DataFrame(l).to_excel("report.xlsx")
|